README.md

---
library_name: pytorch
license: creativeml-openrail-m
tags:
- generative_ai
- quantized
- android
pipeline_tag: unconditional-image-generation

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/stable_diffusion_v2_1_quantized/web-assets/model_demo.png)

# Stable-Diffusion-v2.1: Optimized for Mobile Deployment
## State-of-the-art generative AI model used to generate detailed images conditioned on text descriptions


Generates high resolution images from text prompts using a latent diffusion model. This model uses CLIP ViT-L/14 as text encoder, U-Net based latent denoising, and VAE based decoder to generate the final image.

This model is an implementation of Stable-Diffusion-v2.1 found [here](https://github.com/CompVis/stable-diffusion/tree/main).


This repository provides scripts to run Stable-Diffusion-v2.1 on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/stable_diffusion_v2_1_quantized).


### Model Details

- **Model Type:** Image generation
- **Model Stats:**
  - Input: Text prompt to generate image
  - Text Encoder Number of parameters: 340M
  - UNet Number of parameters: 865M
  - VAE Decoder Number of parameters: 83M
  - Model size: 1GB

| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| TextEncoderQuantizable | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 6.666 ms | 0 - 3 MB | W8A16 | NPU | [Stable-Diffusion-v2.1.so](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/TextEncoderQuantizable.so) |
| TextEncoderQuantizable | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 4.647 ms | 0 - 20 MB | W8A16 | NPU | [Stable-Diffusion-v2.1.so](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/TextEncoderQuantizable.so) |
| TextEncoderQuantizable | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 4.2 ms | 0 - 15 MB | W8A16 | NPU | Use Export Script |
| TextEncoderQuantizable | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 6.84 ms | 0 - 0 MB | W8A16 | NPU | Use Export Script |
| TextEncoderQuantizable | SA7255P ADP | SA7255P | QNN | 88.113 ms | 0 - 9 MB | W8A16 | NPU | Use Export Script |
| TextEncoderQuantizable | SA8255 (Proxy) | SA8255P Proxy | QNN | 6.62 ms | 0 - 3 MB | W8A16 | NPU | Use Export Script |
| TextEncoderQuantizable | SA8650 (Proxy) | SA8650P Proxy | QNN | 6.654 ms | 0 - 2 MB | W8A16 | NPU | Use Export Script |
| TextEncoderQuantizable | SA8775P ADP | SA8775P | QNN | 7.869 ms | 0 - 10 MB | W8A16 | NPU | Use Export Script |
| TextEncoderQuantizable | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 88.113 ms | 0 - 9 MB | W8A16 | NPU | Use Export Script |
| TextEncoderQuantizable | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 6.636 ms | 0 - 3 MB | W8A16 | NPU | Use Export Script |
| TextEncoderQuantizable | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 7.869 ms | 0 - 10 MB | W8A16 | NPU | Use Export Script |
| UnetQuantizable | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 96.977 ms | 0 - 3 MB | W8A16 | NPU | [Stable-Diffusion-v2.1.so](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/UnetQuantizable.so) |
| UnetQuantizable | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 69.178 ms | 0 - 17 MB | W8A16 | NPU | [Stable-Diffusion-v2.1.so](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/UnetQuantizable.so) |
| UnetQuantizable | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 61.668 ms | 0 - 14 MB | W8A16 | NPU | Use Export Script |
| UnetQuantizable | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 99.461 ms | 0 - 0 MB | W8A16 | NPU | Use Export Script |
| UnetQuantizable | SA7255P ADP | SA7255P | QNN | 1467.935 ms | 0 - 7 MB | W8A16 | NPU | Use Export Script |
| UnetQuantizable | SA8255 (Proxy) | SA8255P Proxy | QNN | 98.746 ms | 0 - 2 MB | W8A16 | NPU | Use Export Script |
| UnetQuantizable | SA8650 (Proxy) | SA8650P Proxy | QNN | 97.177 ms | 1 - 3 MB | W8A16 | NPU | Use Export Script |
| UnetQuantizable | SA8775P ADP | SA8775P | QNN | 110.665 ms | 0 - 8 MB | W8A16 | NPU | Use Export Script |
| UnetQuantizable | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 1467.935 ms | 0 - 7 MB | W8A16 | NPU | Use Export Script |
| UnetQuantizable | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 97.457 ms | 0 - 3 MB | W8A16 | NPU | Use Export Script |
| UnetQuantizable | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 110.665 ms | 0 - 8 MB | W8A16 | NPU | Use Export Script |
| VaeDecoderQuantizable | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 295.307 ms | 0 - 71 MB | W8A16 | NPU | [Stable-Diffusion-v2.1.so](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/VaeDecoderQuantizable.so) |
| VaeDecoderQuantizable | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 223.33 ms | 0 - 312 MB | W8A16 | NPU | [Stable-Diffusion-v2.1.so](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/VaeDecoderQuantizable.so) |
| VaeDecoderQuantizable | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 189.418 ms | 0 - 356 MB | W8A16 | NPU | Use Export Script |
| VaeDecoderQuantizable | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 267.095 ms | 0 - 0 MB | W8A16 | NPU | Use Export Script |
| VaeDecoderQuantizable | SA7255P ADP | SA7255P | QNN | 4460.526 ms | 0 - 10 MB | W8A16 | NPU | Use Export Script |
| VaeDecoderQuantizable | SA8255 (Proxy) | SA8255P Proxy | QNN | 274.71 ms | 0 - 2 MB | W8A16 | NPU | Use Export Script |
| VaeDecoderQuantizable | SA8650 (Proxy) | SA8650P Proxy | QNN | 269.652 ms | 0 - 2 MB | W8A16 | NPU | Use Export Script |
| VaeDecoderQuantizable | SA8775P ADP | SA8775P | QNN | 301.141 ms | 0 - 10 MB | W8A16 | NPU | Use Export Script |
| VaeDecoderQuantizable | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 4460.526 ms | 0 - 10 MB | W8A16 | NPU | Use Export Script |
| VaeDecoderQuantizable | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 271.222 ms | 0 - 3 MB | W8A16 | NPU | Use Export Script |
| VaeDecoderQuantizable | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 301.141 ms | 0 - 10 MB | W8A16 | NPU | Use Export Script |




## Installation


Install the package via pip:
```bash
pip install "qai-hub-models[stable-diffusion-v2-1-quantized]" -f https://qaihub-public-python-wheels.s3.us-west-2.amazonaws.com/index.html
```


## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device

Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.

With this API token, you can configure your client to run models on the cloud
hosted devices.
```bash
qai-hub configure --api_token API_TOKEN
```
Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information.



## Demo off target

The package contains a simple end-to-end demo that downloads pre-trained
weights and runs this model on a sample input.

```bash
python -m qai_hub_models.models.stable_diffusion_v2_1_quantized.demo
```

The above demo runs a reference implementation of pre-processing, model
inference, and post processing.

**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.stable_diffusion_v2_1_quantized.demo
```


### Run model on a cloud-hosted device

In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
device. This script does the following:
* Performance check on-device on a cloud-hosted device
* Downloads compiled assets that can be deployed on-device for Android.
* Accuracy check between PyTorch and on-device outputs.

```bash
python -m qai_hub_models.models.stable_diffusion_v2_1_quantized.export
```
```
Profiling Results
------------------------------------------------------------
TextEncoderQuantizable
Device                          : Samsung Galaxy S23 (13)
Runtime                         : QNN                    
Estimated inference time (ms)   : 6.7                    
Estimated peak memory usage (MB): [0, 3]                 
Total # Ops                     : 787                    
Compute Unit(s)                 : NPU (787 ops)          

------------------------------------------------------------
UnetQuantizable
Device                          : Samsung Galaxy S23 (13)
Runtime                         : QNN                    
Estimated inference time (ms)   : 97.0                   
Estimated peak memory usage (MB): [0, 3]                 
Total # Ops                     : 5891                   
Compute Unit(s)                 : NPU (5891 ops)         

------------------------------------------------------------
VaeDecoderQuantizable
Device                          : Samsung Galaxy S23 (13)
Runtime                         : QNN                    
Estimated inference time (ms)   : 295.3                  
Estimated peak memory usage (MB): [0, 71]                
Total # Ops                     : 189                    
Compute Unit(s)                 : NPU (189 ops)          
```





## Deploying compiled model to Android


The models can be deployed using multiple runtimes:
- TensorFlow Lite (`.tflite` export): [This
  tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
  guide to deploy the .tflite model in an Android application.


- QNN (`.so` export ): This [sample
  app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
provides instructions on how to use the `.so` shared library  in an Android application.


## View on Qualcomm® AI Hub
Get more details on Stable-Diffusion-v2.1's performance across various devices [here](https://aihub.qualcomm.com/models/stable_diffusion_v2_1_quantized).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)


## License
* The license for the original implementation of Stable-Diffusion-v2.1 can be found
  [here](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE)



## References
* [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)
* [Source Model Implementation](https://github.com/CompVis/stable-diffusion/tree/main)



## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:[email protected]).